Virtual-assistant-based resolution of user inquiries via failure-triggered document presentation

ABSTRACT

In certain embodiments, document-based resolution of user inquiries may be facilitated. A predicted intent of the user is determined based on chat activity information associated with the user. A response to a user inquiry may be provided to the user via a chat interface based on the predicted intent. User response may be obtained for the response, which may indicate a failure in providing a resolution to the user (e.g., regarding a user inquiry). Upon detecting a failure in providing the resolution, a document associated with the user and matching the predicted intent may be obtained and presented to via the chat interface. The document may have content related to the predicted intent. The document may be presented as a response to the user response to seek a confirmation from the user regarding its relevance to the user inquiry.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.17/118,478, filed Dec. 10, 2020. The content of the foregoingapplication is incorporated herein in its entirety by reference.

TECHNICAL FIELD

The invention relates to conversational artificial intelligence,including, for example, providing resolution of user inquiries viadocument presentation.

BACKGROUND

Chatbots (e.g., a form of automated conversational artificialintelligence or virtual assistant) enable a human user to message orchat with a computer that “talks” like a human and, in some instances,get answers without necessitating human interaction or independentsearches by the user. For example, a chatbot may obtain context from thequestions submitted by the user or answers (or other comments) providedby the user during a chat session and then propose solutions or answersto the user. Often, however, the chatbot may not understand what theuser is asking, the user may not know how to phrase questions to thechatbot, or the user may not be satisfied with the response from thechatbot which causes friction with respect to the user's interactionswith a chatbot service.

SUMMARY

Aspects of the invention relate to methods, apparatuses, and/or systemsfor facilitating virtual-assistant-based resolution of user inquiriesvia document presentation, or training or configuration of neuralnetworks or other prediction models to facilitatevirtual-assistant-based resolution of user inquiries via documentpresentation.

In some embodiments, chat activity information associated with the usermay be provided to a prediction model to obtain a predicted intent ofthe user. A response to a user inquiry may be provided to the user via achat interface based on the predicted intent. User input may be obtainedfor the response, which may indicate a failure in providing a resolutionto the user (e.g., regarding a user inquiry). In some embodiments, upondetecting a failure in providing the resolution, a document associatedwith the user may be obtained from the prediction model based on thepredicted intent and presented to the user via the chat interface. Thedocument may have content related to the predicted intent. The documentmay be presented to seek a confirmation from the user regarding itsrelevance to the user inquiry. In this way, for example, the user isable to clarify or better express the inquiry, or the virtual assistantis able to better understand the inquiry, and the inquiry may beresolved with the user experiencing minimal to no friction with thevirtual assistant.

In some embodiments, the document may be presented to the user based onone or more indicators that indicate a failure in resolving the userinquiry. As an example, an indicator can be that a number of user inputwords or sentences related to seeking the resolution satisfies an amountthreshold. As another example, an indicator can be that a duration ofthe chat session satisfies a duration threshold. As another example, anindicator can be that a negative sentiment is detected in connectionwith the one or more user inputs.

Various other aspects, features, and advantages of the invention will beapparent through the detailed description of the invention and thedrawings attached hereto. It is also to be understood that both theforegoing general description and the following detailed description areexamples and not restrictive of the scope of the invention. As used inthe specification and in the claims, the singular forms of “a,” “an,”and “the” include plural referents unless the context clearly dictatesotherwise. In addition, as used in the specification and the claims, theterm “or” means “and/or” unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and, togetherwith the description, serve to explain the disclosed principles. In thedrawings:

FIG. 1 shows a system for facilitating conversational artificialintelligence, in accordance with one or more embodiments.

FIG. 2 shows a machine learning model configured to facilitate aconversation, in accordance with one or more embodiments.

FIG. 3A shows a chat interface for facilitating user interactions viachat messages, in accordance with one or more embodiments.

FIG. 3B shows another chat interface for facilitating user interactionsvia chat messages, in accordance with one or more embodiments.

FIG. 4 shows a flowchart of a method of facilitating document-basedresolution of user inquiry in a chat interface, in accordance with oneor more embodiments.

FIG. 5 shows a flowchart of a method of obtaining one or documentsmatching the predicted intent, in accordance with one or moreembodiments.

DESCRIPTION OF THE EMBODIMENTS

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It will beappreciated, however, by those having skill in the art that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other cases, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the embodiments of the invention.

FIG. 1 shows a system 100 for facilitating conversational artificialintelligence, in accordance with one or more embodiments. As shown inFIG. 1, system 100 may include computer system 102, client device 104(or client devices 104 a-104 n), or other components. Computer system102 may include document-based resolution subsystem 112, model subsystem114, feedback subsystem 116, presentation subsystem 118, or othercomponents. Each client device 104 may include any type of mobileterminal, fixed terminal, or other device. By way of example, clientdevice 104 may include a desktop computer, a notebook computer, a tabletcomputer, a smartphone, a wearable device, or other client device. Usersmay, for instance, utilize one or more client devices 104 to interactwith one another, one or more servers, or other components of system100. It should be noted that, while one or more operations are describedherein as being performed by particular components of computer system102, those operations may, in some embodiments, be performed by othercomponents of computer system 102 or other components of system 100. Asan example, while one or more operations are described herein as beingperformed by components of computer system 102, those operations may, insome embodiments, be performed by components of client device 104. Itshould be noted that, although some embodiments are described hereinwith respect to machine learning models, other prediction models (e.g.,statistical models or other analytics models) may be used in lieu of orin addition to machine learning models in other embodiments (e.g., astatistical model replacing a machine learning model and anon-statistical model replacing a non-machine-learning model in one ormore embodiments).

In some embodiments, system 100 may facilitate a conversation with orassist a user via one or more prediction models. In some embodiments,system 100 may obtain a document via a prediction model based on apredicted intent of a user and cause the document to be presented on auser interface. In some embodiments, upon receiving a confirmation thatthe document is relevant to a user inquiry, system 100 may proceed withproviding a resolution to the user inquiry. In some embodiments, uponreceiving a confirmation that the document is not relevant to the userinquiry, system 100 may proceed with obtaining and presenting anotherdocument based on the predicted intent. In some embodiments, intentclassification (e.g., automated association of text or actions to aspecific goal or other intent) may be performed on chat information todetermine the document items to be presented on the user interface. Asan example, such intent classification may be performed via a neuralnetwork (e.g., where its last layer or other layer produces aprobability distribution over classes) or one or more other predictionmodels.

In some embodiments, system 100 may obtain the document by providing anumber of documents associated with the user as input to the predictionmodel and obtaining one or more documents based on metadata associatedwith the document matching the predicted intent. As an example, system100 may obtain a document set from the prediction model, where eachdocument of the document set is associated with a probability ofmatching a predicted intent of the user. System 100 may then select oneor more documents from the document set based on the documents beingassociated with higher probabilities (e.g., higher confidence scores ofmatching a predicted intent of the user) than one or more otherdocuments of the document set. The document may have user activityinformation and the metadata may be descriptive of the user activityinformation in a particular document. As an example, the documents maybe provided as input to the prediction model based on one or moreautomated triggers (e.g., upon detecting an indication that an userinquiry is not resolved or other action of the user) to obtain one ormore outputs from the neural network (e.g., via which one or moredocuments having metadata matching the predicted intent are obtained).The user activity information in the document may include serviceinteraction information related to interactions of the user with one ormore services (e.g., the latest interactions or other interactions ofthe user), transaction information related to transactions of the user(e.g., the latest transactions or other transactions of the user), emailinformation related to interactions with or of the user with one or moreservices, or other user activity information (e.g., chat sessioninformation related to a current chat session or prior chat sessionswith the user, information related to upcoming travel or other plans ofthe user, etc.).

In some embodiments, system 100 may obtain one or more predicted intentsof a user via another prediction model (e.g., based on user inputs in achat interface) and generate responses for presentation on the chatinterface based on the predicted intents (e.g., for resolving a userinquiry). As an example, system 100 may obtain an intent set from theprediction model, where each intent of the intent set is associated witha probability of matching a current intent of the user. System 100 maythen select the predicted intents from the intent set based on thepredicted intents being associated with higher probabilities (e.g.,higher confidence scores of matching a current intent of the user) thanone or more other predicted intents of the intent set.

In some embodiments, system 100 may train or configure a predictionmodel to facilitate a conversation with or assist one or more users. Insome embodiments, system 100 may obtain user input informationassociated with a user (e.g., chat activity information regarding a userinquiry in a chat interface) and provide such information as input to aprediction model to generate predictions (e.g., related to an intent ofthe user, such as a goal of the user, a question or statement that theuser intends to submit, etc.). System 100 may provide reference feedbackto the prediction model and the prediction model may update one or moreportions of the prediction model based on the predictions and thereference feedback. As an example, where the prediction model generatespredictions based on user input information coinciding with a given timeperiod, one or more verified intents associated with such useractivities may be provided as reference feedback to the predictionmodel. As an example, a particular goal may be verified as the user'sintent (e.g., via user confirmation of the goal, via one or moresubsequent actions demonstrating such goal, etc.) based on one or moreuser responses or one or more other user actions via one or moreservices. The foregoing user input information may be provided as inputto the prediction model to cause the prediction model to generatepredictions of the user's intent, and the verified goal may be providedas reference feedback to the prediction model to update the predictionmodel. In this way, for example, the prediction model may be trained orconfigured to generate more accurate predictions.

In some embodiments, system 100 may also train or configure a predictionmodel to facilitate obtaining a document associated with the user basedon the predicted intent. In some embodiments, system 100 may obtaindocuments associated with a user (e.g., documents having user activityinformation) and provide them as input to a prediction model to generatepredictions (e.g., whether each document is matching the predictedintent). System 100 may provide reference feedback to the predictionmodel, and the prediction model may update one or more portions of theprediction model based on the predictions and the reference feedback. Asan example, where the prediction model generates predictions based onthe document information (e.g., content of document), one or moreverified concepts (e.g., topic, category, etc.) associated with suchdocuments may be provided as reference feedback to the prediction model.As an example, a particular document may be verified as matching thepredicted intent (e.g., via user confirmation of the document presentedto the user, via one or more subsequent actions demonstrating such goal,etc.) when the user responds to the presentation of the document in thechat interface. The foregoing document information may be provided asinput to the prediction model to cause the prediction model to generatepredictions of whether a document matches the predicted intent, and theverified goal may be provided as reference feedback to the predictionmodel to update the prediction model. In this way, for example, theprediction model may be trained or configured to generate more accuratepredictions.

In some embodiments, the foregoing operations for updating theprediction model may be performed with a training dataset with respectto one or more users (e.g., a training dataset associated with a givenuser to specifically train or configure the prediction model for thegiven user, a training dataset associated with a given cluster,demographic, or other group to specifically train or configure theprediction model for the given group, or other training dataset). Assuch, in some embodiments, subsequent to the updating of the predictionmodel, system 100 may use the prediction model to facilitate aconversation with or assist one or more users.

In some embodiments, the prediction model may include one or more neuralnetworks or other machine learning models. As an example, neuralnetworks may be based on a large collection of neural units (orartificial neurons). Neural networks may loosely mimic the manner inwhich a biological brain works (e.g., via large clusters of biologicalneurons connected by axons). Each neural unit of a neural network may beconnected with many other neural units of the neural network. Suchconnections can be enforcing or inhibitory in their effect on theactivation state of connected neural units. In some embodiments, eachindividual neural unit may have a summation function which combines thevalues of all its inputs together. In some embodiments, each connection(or the neural unit itself) may have a threshold function such that thesignal must surpass the threshold before it propagates to other neuralunits. These neural network systems may be self-learning and trained,rather than explicitly programmed, and can perform significantly betterin certain areas of problem solving, as compared to traditional computerprograms. In some embodiments, neural networks may include multiplelayers (e.g., where a signal path traverses from front layers to backlayers). In some embodiments, back propagation techniques may beutilized by the neural networks, where forward stimulation is used toreset weights on the “front” neural units. In some embodiments,stimulation and inhibition for neural networks may be more free-flowing,with connections interacting in a more chaotic and complex fashion.

As an example, with respect to FIG. 2, machine learning model 202 maytake inputs 204 and provide outputs 206. In one use case, outputs 206may be fed back to machine learning model 202 as input to train machinelearning model 202 (e.g., alone or in conjunction with user indicationsof the accuracy of outputs 206, labels associated with the inputs, orwith other reference feedback information). In another use case, machinelearning model 202 may update its configurations (e.g., weights, biases,or other parameters) based on its assessment of its prediction (e.g.,outputs 206) and reference feedback information (e.g., user indicationof accuracy, reference labels, or other information). In another usecase, where machine learning model 202 is a neural network, connectionweights may be adjusted to reconcile differences between the neuralnetwork's prediction and the reference feedback. In a further use case,one or more neurons (or nodes) of the neural network may require thattheir respective errors are sent backward through the neural network tothem to facilitate the update process (e.g., backpropagation of error).Updates to the connection weights may, for example, be reflective of themagnitude of error propagated backward after a forward pass has beencompleted. In this way, for example, the machine learning model 202 maybe trained to generate better predictions.

Subsystems 112-118

In some embodiments, document-based resolution subsystem 112 may obtaina document via a prediction model, and presentation subsystem 118 maycause the document to be presented on a user interface for a user. Insome embodiments, the document-based resolution subsystem 112 may obtainthe document in response to detecting an indication of a failure inresolving a user inquiry. As an example, the document may be presentedon the user interface in connection with seeking a confirmation from theuser regarding the document's relevance to the user inquiry. In someembodiments, upon receiving a user confirmation that the document isrelevant to the user inquiry, computer system 102 may proceed withproviding a resolution to the user inquiry. As an example, the documentmay be presented in a chat interface 302 of FIG. 3A in connection withresolving a user inquiry. FIGS. 3A and 3B show a chat interface forfacilitating user interactions via chat messages, in accordance with oneor more embodiments. A user may access the chat interface 302 using aclient device 104 a. The chat interface 302 facilitates the user tointeract with computer system 102 via chat messages for resolving aninquiry. The chat interface 302 may be associated with an applicationthat provides services or products. As an example, the chat interface302 may be associated with a financial services application thatprovides financial products or services.

The chat interface 302 facilitates the user to resolve a user inquiryvia a conversation (e.g., chat messages). For example, the user mayinitiate a chat session by inputting an inquiry such as “Will I pay atransaction fee?” The user input is provided to model subsystem 114 forobtaining an output response that either resolves the inquiry or seeksfurther information from the user for resolving the inquiry. Forexample, model subsystem 114 may generate an output response requestinginformation, such as “Are you referring to a credit card?” and providethe output response to presentation subsystem 118 for presentation inthe chat interface 302. The user may further input a response to therequest, such as “yes,” and the model subsystem 114 may generate asuitable response. As an example, model subsystem 114 may predict anintent of the user based on the user input information in the chatsession and generate the output responses based on the predicted intent.Such interactions between the user and computer system 102 may continueuntil the user inquiry is resolved, or the chat session is otherwiseterminated (e.g., terminated by the user or computer system 102).

In some embodiments, document-based resolution subsystem 112 may detectan indication of a failure in resolving the user inquiry (e.g., userindication or other automated triggers). As an example, document-basedresolution subsystem 112 may detect an indication of a failure when anumber of user input words or sentences related to seeking theresolution satisfies an amount threshold (e.g., greater than or equal tothe amount threshold). As another example, an indicator can be that aduration of the chat session satisfies a duration threshold (e.g.,greater than or equal to the duration threshold). As another example, anindicator can be that a negative sentiment is detected in connectionwith the user inputs. In the example of FIG. 3A, at the time when thechat interface 302 receives user input 306, document-based resolutionsubsystem 112 may determine that the user inquiry is still not resolvedand the number of words or sentences between user input 304 and userinput 306 has satisfied an amount threshold (e.g., 30 words, 10sentences, or other amount) and accordingly may detect an indication ofa failure in resolving the query. As another example, at the time whenthe chat interface 302 receives user input 306, document-basedresolution subsystem 112 may determine that the user inquiry is stillnot resolved and the duration of the chat session has satisfied aduration threshold (e.g., 5 minutes, 8 minutes, or other duration) andaccordingly may detect an indication of a failure in resolving thequery. As another example, at the time when the chat interface 302receives user input 308, document-based resolution subsystem 112 maydetermine that the user inquiry is still not resolved and determine anegative sentiment (e.g., frustration, anger, or other negativesentiment) of the user based on the user input 308, and accordingly maydetect an indication of a failure in resolving the query.

In some embodiments, document-based resolution subsystem 112 may performsentiment analysis monitoring to determine the sentiment associated withthe user. As an example, the user input information may be provided asinput to a sentiment analysis prediction model to obtain a polarity(e.g., a positive or negative sentiment) within word, phrase, sentence,or clause of the user input information. In some embodiments, feelingsand emotions (e.g., angry, happy, sad, etc.) may also be obtained fromthe sentiment analysis prediction model. In some embodiments, thesentiment analysis prediction model may use various Natural LanguageProcessing (NLP) methods or other methods to determine the sentiment. Asan example, the sentiment analysis prediction model may use a set ofuser-defined rules (e.g., rules defined by an administrator associatedwith system 100 or other user) to identify the sentiment. These rulesmay define two lists of polarized words (e.g., negative words such asbad, worst, ugly, etc., and positive words such as good, best,beautiful, etc.). The sentiment analysis prediction model may count thenumber of positive and negative words that appear in an input text(e.g., user input 308) and determine a negative sentiment based on thenumber of negative word appearances in the input text being greater thanthe number of positive word appearances, and vice versa. As anotherexample, the sentiment analysis prediction model may be trained toclassify the input text into a category (e.g., a positive, negative, orneutral sentiment.) In the training process, feature vectors extractedfrom a particular input (e.g., user input or response) and a labelassociated with the corresponding input, such as a negative, positive,or neutral sentiment, are fed to the sentiment analysis prediction modelas feature vector and label pairs. The sentiment analysis predictionmodel learns to associate each of the feature vectors to thecorresponding label based on the training data (e.g., user input orresponse gathered from various chat sessions and that are associatedwith a label) used for training. In the prediction process, a featurevector of an unseen text (e.g., user input or response, such as userinput 308 from chat session) is extracted and input to the sentimentanalysis prediction model, which generates predicted labels (e.g., apositive, negative, or neutral sentiment).

In response to detecting an indication of failure in resolving the userinquiry, document-based resolution subsystem 112 may obtain a document310, via a prediction model, and presentation subsystem 118 may presentthe document 310 in the chat interface 302. In some embodiments,document 310 may include information such as service interactioninformation related to interactions of the user with one or moreservices (e.g., the latest interactions or other interactions of theuser), transaction information related to transactions of the user(e.g., the latest transactions or other transactions of the user), emailinformation related to interactions with or of the user with one or moreservices, or other user activity information (e.g., chat sessioninformation related to a current chat session or prior chat sessionswith the user, information related to upcoming travel or other plans ofthe user, etc.). As an example, the document 310 may be a utility billassociated with the user, a credit card, debit card, or other financialaccount statement, an email from one or more services, one or moreimages having various content information, etc. The document 310 may bepresented in the chat interface 302 in connection with seeking aconfirmation from the user regarding the relevance of the document 310to the user inquiry. Responsive to obtaining a user response to thepresentation of the document 310, model subsystem 114 may generate aresponse to the user response (e.g., for resolving the user inquiry),and presentation subsystem 118 may cause the response to be presented onthe chat interface 302. In some embodiments, model subsystem 114 maygenerate the response (e.g., an answer to a selected question, etc.)based on the user response received from the user. For example, if theuser response to the presentation of the document 310 is “Yes,” modelsubsystem 114 may generate a response such as “There is no foreigntransaction fee for your card” or other such response for resolving theuser inquiry “Will I pay a foreign transaction fee?”

In the example of FIG. 3B, model subsystem 114 may, for each of theuser's response or input information in the chat session, predict thesame intent (e.g., amount owed by the user on their credit cardstatement) and generate the output responses based on the predictedintent. However, at the time when the chat interface 352 receives userinput 356, document-based resolution subsystem 112 may determine thatthe user inquiry is still not resolved and the number of words orsentences between user input 354 and user input 356 has satisfied anamount threshold (e.g., 30 words, 10 sentences, or other amount) andaccordingly may detect an indication of a failure in resolving thequery. In response to detecting an indication of failure in resolvingthe user inquiry, document-based resolution subsystem 112 may obtain adocument 360, via a prediction model, and presentation subsystem 118 maypresent the document 360 in the chat interface 352. As an example, thedocument 360 may be a credit card statement associated with the user.The document 360 may be presented in the chat interface 352 inconnection with seeking a confirmation from the user regarding therelevance of the document 360 to the user inquiry. For example, if theuser response to the presentation of the document 360 is “Yes,” modelsubsystem 114 may generate a response such as “$999 is the amount youowe” or other such response based on the information in document 360 forresolving the user inquiry “How much do I owe on my card?”

In some embodiments, document-based resolution subsystem 112 obtains thedocument 310 via a prediction model based on metadata of the document310 matching a predicted intent of the user. As an example, the metadataof the document 310 may include information regarding content of thedocument 310, such as a concept (e.g., a topic, a category, an interest,or other information) associated with the document 310 or one or moreportions of the document 310, transaction information of transactions inthe document 310 (e.g., electricity bill information, credit cardspending information, transaction amount, transaction date, etc.) orother such information. In some embodiments, document-based resolutionsubsystem 112 may extract content of the document 310 (e.g., usingoptical character recognition or other methods) and generate metadata ofthe document 310 based on the extracted content. In some embodiments,obtaining the document 310 may include document-based resolutionsubsystem 112 providing a number of documents associated with the useras input to the prediction model to obtain concepts (e.g., a topic, acategory, an interest, or other information) associated with each of thedocuments, and assigning a score to each of the documents that isindicative of a probability of a match between the document and thepredicted intent of the user based on the concepts of the document. Insome embodiments, document-based resolution subsystem 112 may obtain thedocument having the highest score as the document 310 for presentationto the user. In some embodiments, document-based resolution subsystem112 may obtain a document set from the prediction model and select asubset of documents associated with higher probabilities (e.g., higherconfidence scores of matching a predicted intent of the user) than oneor more other documents of the document set for presentation to theuser.

In some embodiments, document-based resolution subsystem 112 may presentthe subset of documents to the user based on user responses obtained inresponse to presentation of a document of the subset of documents. Forexample, presentation subsystem 118 may present a first document of thesubset of documents (e.g., such as document 310) in response todetection of a failure in resolution of the user inquiry. The user mayview the first document and provide a user response in response to thepresentation of the first document. For example, if the user response tothe presentation of the first document confirms a relevance of the firstdocument to the user inquiry, model subsystem 114 may generate aresponse for resolving the user inquiry. However, if the user responseto the presentation of the first document confirms that the firstdocument is not relevant to the user inquiry, document-based resolutionsubsystem 112 may obtain a second document from the subset of thedocuments for presentation to the user.

In some embodiments, document-based resolution subsystem 112 mayidentify a document portion 312 of the document 310 that is relevant tothe predicted intent more than other document portions of the document310. For example, document-based resolution subsystem 112 may obtain,via the prediction model, concepts associated with various documentportions of the document 310, and assign each document portion a scorethat is indicative of a probability of the document portion matching thepredicted intent of the user. Document-based resolution subsystem 112may then select one or more document portions from the document based onthe document portions being associated with higher probabilities (e.g.,higher confidence scores of matching the predicted intent of the user)than one or more other document portions of the document forpresentation to the user.

The presentation subsystem 118 may present the document 310 or thedocument portion 312 in the chat interface 302 in a number of ways. Forexample, presentation subsystem 118 may present a link corresponding todocument 310 in the chat interface 302, which, when selected by theuser, presents document 310 to the user in the client device 104 a. Inanother example, presentation subsystem 118 may present an imagerepresentative of document 310 (e.g., a thumbnail) in the chat interface302 which, when selected by the user, presents document 310 to the user.In another example, presentation subsystem 118 may present document 310(e.g., a thumbnail) in the chat interface 302 for downloading by theuser to the client device 104 a. In another example, presentationsubsystem 118 may present an image depicting one or more documentportions of document 310, where document portion 312 is emphasized toindicate that document portion 312 is relevant to the predicted intentmore than the other document portions of document 310. In anotherexample, presentation subsystem 118 may present an image having documentportion 312 which, when selected by the user, presents document 310 tothe user with document portion 312 emphasized in document 310.Presentation subsystem 118 may emphasize document portion 312 in anumber of ways. For example, presentation subsystem 118 may emphasizedocument portion 312 by highlighting text in document portion 312 in aspecified color, making the text in document portion 312 of a differentfont, size, style from that of the text in the other document portions,or provide other such emphasis. In another example, presentationsubsystem 118 may emphasize document portion 312 by enclosing documentportion 312 in an outline (such as a rectangular outline as shown inFIG. 3A).

In some embodiments, document-based resolution subsystem 112 mayfacilitate the user to upload a file (e.g., a document, an image, orother file) that may be used by model subsystem 114 to further predictthe intent of the user in resolving the user inquiry. For example, modelsubsystem 114 may provide the uploaded document to a prediction model topredict an intent of the user based on content of the uploaded file. Insome embodiments, document-based resolution subsystem 112 may havepresentation subsystem 118 present an upload option 314 in chatinterface 302 for facilitating the user to upload a file to computersystem 102. The document-based resolution subsystem 112 may control thepresentation of the upload option 314 in the chat interface 302 invarious ways. For example, presentation subsystem 118 may present theupload option 314 in chat interface 302 when the chat session starts,and the user may upload the file anytime during the chat session. Inanother example, presentation subsystem 118 may present the uploadoption 314 in response to document-based resolution subsystem 112detecting a failure in resolution of the user inquiry. In anotherexample, presentation subsystem 118 may present the upload option 314 inresponse to document-based resolution subsystem 112 detecting a failurein resolution of the user inquiry even after presentation of one or moredocuments, such as after presentation of document 310, to the user.

In some embodiments, model subsystem 114 may provide chat activityinformation associated with the user (e.g., chat messages exchangedbetween the user and computer system 102) as input to a prediction modelto obtain one or more predicted intents of the user, and presentationsubsystem 118 may cause one or more responses to be presented on a userinterface (e.g., chat interface 302) based on the predicted intents ofthe user. As an example, with reference to chat interface 302, modelsubsystem 114 may provide one or more messages exchanged with the user(e.g., messages input by the user or messages presented to the user),such as messages 304-316, to the prediction model to obtain thepredicted intent. In some embodiments, model subsystem 114 may obtain anintent set from the prediction model and select one or more predictedintents from the intent set, where each predicted intent of the intentset is associated with a probability of matching a current intent of theuser. In some embodiments, one or more predicted intents may be selectedfrom the intent set based on a determination that the probabilities ofthe predicted intents are greater than or equal to the probabilities ofother predicted intents of the intent set. Additionally, oralternatively, the predicted intents may be selected from the intent setbased on a determination that the probabilities of the predicted intentssatisfy one or more probability thresholds (e.g., greater than or equalto a minimum confidence score threshold).

In some embodiments, model subsystem 114 may train or configure a firstprediction model to facilitate a conversation with or assist one or moreusers. In some embodiments, model subsystem 114 may obtain chat activityinformation associated with a number of users and provide suchinformation as input to the first prediction model to generatepredictions (e.g., related to an intent of the user, such as a goal ofthe user, a question or statement that the user intends to submit,etc.). Additionally, or alternatively, account information or otherinformation associated with the user may be provided as input to thefirst prediction model to generate such predictions. Feedback subsystem116 may provide reference feedback to the first prediction model, andthe first prediction model may update one or more portions of theprediction model based on the predictions and the reference feedback. Inone use case, where the first prediction model includes a neuralnetwork, the neural network may assess its predictions (e.g., itspredicted intents, their associated confidence scores, or otherprobabilities, etc.) against the reference feedback (e.g., verifiedintents). The neural network may then update its weights, biases, orother parameters based on the prediction assessment. In someembodiments, the foregoing operations for updating the first predictionmodel may be performed with a training dataset with respect to one ormore users (e.g., a training dataset associated with a given user tospecifically train or configure the first prediction model for the givenuser, a training dataset associated with a given cluster, demographic,or other group to specifically train or configure the first predictionmodel for the given group, or other training dataset).

As an example, where the first prediction model generates predictionsbased on user chat activity information coinciding with a given timeperiod, one or more verified intents associated with such user chatactivity information may be provided as reference feedback to theprediction model. In one use case, a particular goal may be verified asthe user's intent (e.g., via user confirmation of the goal, via one ormore subsequent actions demonstrating such goal, etc.) based on one ormore user responses or one or more other user actions via one or moreservices. The foregoing user chat activity information may be providedas input to the first prediction model to cause the first predictionmodel to generate predictions of the user's intent, and the verifiedgoal may be provided as reference feedback to the first prediction modelto update the prediction model. In this way, for example, the firstprediction model may be trained or configured to generate more accuratepredictions.

In some embodiments, document-based resolution subsystem 112 may trainor configure a second prediction model to facilitate obtaining adocument associated with the user based on the predicted intent (e.g.,obtained from the first prediction model). In some embodiments,document-based resolution subsystem 112 may obtain documents associatedwith a user (e.g., documents having user activity information) andprovide them as input to a second prediction model to generatepredictions (e.g., whether each document is matching the predictedintent). Additionally, or alternatively, account information or otherinformation associated with the user (e.g., which may provide access tovarious user information) may be provided as input to the secondprediction model to generate such predictions. Feedback subsystem 116may provide reference feedback to the second prediction model, and thesecond prediction model may update one or more portions of the secondprediction model based on the predictions and the reference feedback. Inone use case, where the second prediction model includes a neuralnetwork, the neural network may assess its predictions (e.g., itspredicted intents, their associated confidence scores, or otherprobabilities, etc.) against the reference feedback (e.g., verifiedintents). The neural network may then update its weights, biases, orother parameters based on the prediction assessment. In someembodiments, the foregoing operations for updating the second predictionmodel may be performed with a training dataset with respect to one ormore users (e.g., a training dataset associated with a given user tospecifically train or configure the second prediction model for thegiven user, a training dataset associated with a given cluster,demographic, or other group to specifically train or configure thesecond prediction model for the given group, or other training dataset).

As an example, where the second prediction model generates predictionsbased on document information (e.g., content of the document), one ormore verified concepts (e.g., topic, category, etc.) associated withsuch documents may be provided as reference feedback to the secondprediction model. As an example, a particular document may be verifiedas matching the predicted intent (e.g., via user confirmation of thedocument presented to the user, via one or more subsequent actionsdemonstrating such goal, etc.) when the user responds to thepresentation of the document in the chat interface. The foregoingdocument information may be provided as input to the second predictionmodel to cause the second prediction model to generate predictions ofdocuments matching the predicted intent, and the verified goal may beprovided as reference feedback to the second prediction model to updatethe second prediction model. In this way, for example, the secondprediction model may be trained or configured to generate more accuratepredictions.

In some embodiments, presentation subsystem 118 may cause one or moredocuments (e.g., document 310) to be presented on a user interface basedon one or more predicted intents of a user (e.g., provided via aprediction model), and document-based resolution subsystem 112 mayobtain a user response to presentation of a document (or the documents)via the user interface. Based on the user response and the firstdocument corresponding to a first intent (of the predicted intents)(e.g., the first document matches the first intent), feedback subsystem116 may use the first intent to update one or more configurations of theprediction model (e.g., one or more weights, biases, or other parametersof the prediction model). In one use case, feedback subsystem 116 mayprovide the first intent as reference feedback to the prediction model,and, in response, the prediction model may assess its predicted intents(and/or their associated probabilities) against the first intent (and/orits associated probabilities). Based on its assessment, the predictionmodel may update one or more weights, biases, or other parameters of theprediction model. In this way, for example, the user selection of thepresented documents may be used to further train or configure theprediction model (e.g., specifically for the user, specifically for agroup associated with the user, etc.).

In some embodiments, where the user response indicates that a presenteddocument does not match the user's current intent or provides adifferent response (e.g., uploads a file or document different from thepresented documents), the foregoing response (or lack thereof) may beused to update one or more portions of the prediction model, an intentset from which one or more intents is to be predicted, or a document setfrom which one or more documents may be selected. As an example, whenthe user response indicates that none of the presented documents matchthe current intent of the user and provides another file or document(e.g., uploaded by the user via chat interface 302), document-basedresolution subsystem 112 may provide the uploaded document to aprediction model to obtain the concepts associated with uploadeddocument or perform natural language processing to determine one or moreintents associated with the user. In some embodiments, the intent set orthe document set may be subsets of larger collection of potentialintents and documents, and the intent set or the document sets may beupdated based on the user's responses so that the correct document(s) ordocument portion(s) may be presented when the user returns in the sameor substantially similar circumstance.

In some embodiments, where the user selects a presented documentcorresponding to a predicted intent (e.g., obtained from a predictionmodel), feedback subsystem 116 may determine a feedback score associatedwith the predicted intent (e.g., based on the user selecting thecorresponding document) or the predicted document and use the feedbackscore to update one or more configurations of the prediction model(e.g., one or more weights, biases, or other parameters of theprediction model). In some embodiments, feedback subsystem 116 mayprovide the feedback score as reference feedback to the predictionmodel, and, in response, the prediction model may assess its predictedintents (and/or their associated probabilities) or predicted documentsbased on the feedback score. Based on its assessment, the predictionmodel may update one or more weights, biases, or other parameters of theprediction model.

In some embodiments, feedback subsystem 116 may determine the feedbackscore for the predicted intent or the predicted document based on aprobability associated with the predicted intent the predicted document,a category associated with the predicted intent or the predicteddocument, or other criteria. As an example, the feedback score may be aconfidence score associated with the predicted intent or the predicteddocument, or the feedback score may be calculated based on theconfidence score. As another example, where the predicted intent or thepredicted document is associated with a probability (e.g., a confidencescore or other probability) in a given tier, feedback subsystem 116 maydetermine the feedback score based on the tier in which the probabilityof the predicted intent or the predicted document resides.

Example Flowchart(s)

The example flowchart(s) described herein of processing operations ofmethods that enable the various features and functionality of the systemas described in detail above. The processing operations of each methodpresented below are intended to be illustrative and non-limiting. Insome embodiments, for example, the methods may be accomplished with oneor more additional operations not described, and/or without one or moreof the operations discussed. Additionally, the order in which theprocessing operations of the methods are illustrated (and describedbelow) is not intended to be limiting.

In some embodiments, the methods may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The processingdevices may include one or more devices executing some or all of theoperations of the methods in response to instructions storedelectronically on an electronic storage medium. The processing devicesmay include one or more devices configured through hardware, firmware,and/or software to be specifically designed for execution of one or moreof the operations of the methods.

FIG. 4 shows a flowchart of a method 400 of facilitating document-basedresolution of user inquiry in a chat interface, in accordance with oneor more embodiments. In an operation 402, a chat initiation request maybe obtained from a customer. As an example, upon obtaining the chatinitiation request, a chat session may be initiated, and a chatinterface may be provided for presentation to the customer (e.g., on aclient device 104 a). Operation 402 may be performed by a subsystem thatis the same as or similar to presentation subsystem 118, in accordancewith one or more embodiments.

In an operation 404, user input information associated with a customerinquiry may be obtained from the customer. As an example, the user inputinformation may be associated with a customer inquiry and may includeone or more chat messages exchanged with the customer in chat interface302 (e.g., messages 304-316). Operation 402 may be performed by asubsystem that is the same as or similar to presentation subsystem 118,in accordance with one or more embodiments.

In an operation 406, user input information may be provided as input toa neural network to obtain a predicted intent of the customer. As anexample, the user input information, such as messages 304-316, may beprovided as input to the neural network after a predetermined number ofmessages are obtained from the customer. As another example, the userinput information may be provided as input to the neural network on aperiodic basis, in accordance with a schedule, or based on one or moreother automated triggers to obtain one or more outputs from the neuralnetwork (e.g., via which predicted intents are obtained). Operation 406may be performed by a subsystem that is the same as or similar to modelsubsystem 114, in accordance with one or more embodiments.

In an operation 408, a first response may be obtained based on thepredicted intent and presented on the chat interface. As an example, afirst response, such as message 318, for resolving the customer inquiryor seeking further information regarding the customer inquiry may bepresented on the chat interface. Operation 408 may be performed by asubsystem that is the same as or similar to model subsystem 114 andpresentation subsystem 118, in accordance with one or more embodiments.

In an operation 410, a second user input may be obtained from thecustomer via the chat interface subsequent to the first response, wherethe second input may include one or more messages regarding the customerinquiry or the first response. As an example, messages 320-306 may bereceived from the customer responsive to the first response fromcomputer system 102 (e.g., message 318). Operation 410 may be performedby a subsystem that is the same as or similar to model subsystem 114 orpresentation subsystem 118, in accordance with one or more embodiments.

In an operation 412, based on the second user input, an indication offailure in providing resolution to the customer inquiry may be detected.As an example, an indication of the failure may be detected when anumber of user input words or sentences (e.g., messages 304-306) relatedto seeking the resolution satisfies an amount threshold. As anotherexample, an indication of the failure may be detected when a duration ofthe chat session satisfies a duration threshold. As another example, anindication of the failure may be detected when a negative sentiment isdetected in connection with the second user input (e.g., message 308).Operation 412 may be performed by a subsystem that is the same as orsimilar to document-based resolution subsystem 112, in accordance withone or more embodiments.

In an operation 414, one or more documents associated with the customermay be provided as input to a neural network to obtain a documentmatching the predicted intent of the customer. As an example, document310 may be obtained via the neural network. In some embodiments, adocument portion of the document that is relevant to the predictedintent more than other document portions of the document may also bedetermined by the neural network. Additional details with reference toobtaining the document or the document portion matching the predictedintent is described at least with reference to FIG. 5 below. In someembodiments, document 310 may include information such as serviceinteraction information related to interactions of the user with one ormore services (e.g., the latest interactions or other interactions ofthe user), transaction information related to transactions of the user(e.g., the latest transactions or other transactions of the user), emailinformation related to interactions with or of the user with one or moreservices, or other user activity information (e.g., chat sessioninformation related to a current chat session or prior chat sessionswith the user, information related to upcoming travel or other plans ofthe user, etc.). As an example, the document 310 may be a utility billassociated with the user, a credit card related document, a debit cardrelated document, or other financial account statement, an email fromone or more services, one or more images having various contentinformation, etc. Operation 414 may be performed by a subsystem that isthe same as or similar to document-based resolution subsystem 112, inaccordance with one or more embodiments.

In an operation 416, the document is presented in the chat interface asa response to the second user input. As an example, the document 310 ispresented in the chat interface 302 in response to the messages 320-306.In some embodiments, the document 310 may be presented in the chatinterface 302 in connection with seeking a confirmation from thecustomer regarding the relevance of the document 310 to the customerinquiry. As an example, responsive to obtaining a user responseconfirming the relevance of the document 310 to the customer inquiry, aresponse to the user response (e.g., for resolving the user inquiry) maybe presented in the chat interface. As another example, responsive toobtaining a user response confirming the document 310 is not relevant tothe customer inquiry, another document (e.g., from a document setmatching the predicted intent) matching the predicted intent may bepresented in the chat interface. In some embodiments, a document portionof the document that is relevant to the predicted intent more than otherdocument portions of the document may be presented in the chatinterface. The document or document portion may be presented in the chatinterface in a number of ways. For example, a link corresponding todocument may be presented in the chat interface. In another example, animage representative of the document (e.g., a thumbnail) may bepresented which, when selected by the user, the entire document or thedocument portion is presented to the user. In another example, an imagehaving one or more document portions may be presented, where thedocument portion that is relevant to the predicted intent more than theother document portions of the document is emphasized. Operation 416 maybe performed by a subsystem that is the same as or similar todocument-based resolution subsystem 112 or presentation subsystem 118,in accordance with one or more embodiments.

FIG. 5 shows a flowchart of a method 500 of obtaining one or documentsmatching the predicted intent, in accordance with one or moreembodiments. In some embodiments, the method 500 may be implemented aspart of operation 414 of method 400 of FIG. 4. In an operation 502,documents associated with a customer may be obtained. As an example, thedocuments associated with the customer may be obtained from a useraccount associated with one or more services with which the customer hasregistered, from the customer, or another source.

In an operation 504, the documents are provided as input to a neuralnetwork to obtain concepts associated with each of the documents. As anexample, a concept may include a topic, category, an interest, or othersuch information associated with a document. In some embodiments, theconcepts are obtained by analyzing the content (e.g., text) of each ofthe documents. The metadata of the documents may be updated accordingly.As an example, the metadata of the document may include informationregarding content of the document, such as a concept associated with thedocument or one or more document portions of the document, transactioninformation of transactions in the document (e.g., electricity billinformation, credit card spending information, transaction amount,transaction date, etc.) or other such information. Operation 504 may beperformed by a subsystem that is the same as or similar todocument-based resolution subsystem 112, in accordance with one or moreembodiments.

In an operation 506, a probability of each document matching thepredicted intent may be determined based on the concepts associated withthe document. As an example, a confidence score may be assigned to eachof the documents that is indicative of a probability of the match, wherethe higher the confidence score the higher is the probability of thematch. In some embodiments, a probability of one or more documentportions of the document matching the predicted intent may also bedetermined based on the concepts associated with the document portions.Operation 506 may be performed by a subsystem that is the same as orsimilar to document-based resolution subsystem 112, in accordance withone or more embodiments.

In an operation 508, one or more documents may be selected based ontheir probabilities satisfying a probability threshold (e.g., greaterthan or equal to a minimum probability threshold). As an example, one ormore documents may be selected based on their confidence scoressatisfying a threshold (e.g., greater than or equal to a minimumconfidence score threshold). In some embodiments, one or more documentportions of the document may also be selected based on theirprobabilities satisfying a probability threshold (e.g., greater than orequal to a minimum probability threshold). In some embodiments, a newdocument may be generated from the selected documents or other availableuser information and the new document may be presented in the chatinterface. As an example, the documents associated with the user includemonthly credit card statements, and the user inquiry may be aboutcharges associated with a specified merchant for a specified quarter.Such “not-readily available” user requested information may be obtainedby providing one or available documents or other transaction informationof the user (e.g., credit card charge information stored in a database)as input to document-based resolution subsystem 112, which processes thecontent of the available documents (e.g., extract content and processthe extracted content) or other transaction information to obtain therequest information and generate a document with the requestedinformation. Operation 508 may be performed by a subsystem that is thesame as or similar to document-based resolution subsystem 112, inaccordance with one or more embodiments.

In some embodiments, the various computers and subsystems illustrated inFIG. 1 may include one or more computing devices that are programmed toperform the functions described herein. The computing devices mayinclude one or more electronic storages (e.g., prediction database(s)132, which may include training data database(s) 134, model database(s)136, etc., or other electronic storages), one or more physicalprocessors programmed with one or more computer program instructions,and/or other components. The computing devices may include communicationlines or ports to enable the exchange of information within a network(e.g., network 150) or other computing platforms via wired or wirelesstechniques (e.g., Ethernet, fiber optics, coaxial cable, WiFi,Bluetooth, near field communication, or other technologies). Thecomputing devices may include a plurality of hardware, software, and/orfirmware components operating together. For example, the computingdevices may be implemented by a cloud of computing platforms operatingtogether as the computing devices.

The electronic storages may include non-transitory storage media thatelectronically stores information. The storage media of the electronicstorages may include one or both of (i) system storage that is providedintegrally (e.g., substantially non-removable) with servers or clientdevices or (ii) removable storage that is removably connectable to theservers or client devices via, for example, a port (e.g., a USB port, afirewire port, etc.) or a drive (e.g., a disk drive, etc.). Theelectronic storages may include one or more of optically readablestorage media (e.g., optical disks, etc.), magnetically readable storagemedia (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.),electrical charge-based storage media (e.g., EEPROM, RAM, etc.),solid-state storage media (e.g., flash drive, etc.), and/or otherelectronically readable storage media. The electronic storages mayinclude one or more virtual storage resources (e.g., cloud storage, avirtual private network, and/or other virtual storage resources). Theelectronic storage may store software algorithms, information determinedby the processors, information obtained from servers, informationobtained from client devices, or other information that enables thefunctionality as described herein.

The processors may be programmed to provide information processingcapabilities in the computing devices. As such, the processors mayinclude one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. In someembodiments, the processors may include a plurality of processing units.These processing units may be physically located within the same device,or the processors may represent processing functionality of a pluralityof devices operating in coordination. The processors may be programmedto execute computer program instructions to perform functions describedherein of subsystems 112-118 or other subsystems. The processors may beprogrammed to execute computer program instructions by software;hardware; firmware; some combination of software, hardware, or firmware;and/or other mechanisms for configuring processing capabilities on theprocessors.

It should be appreciated that the description of the functionalityprovided by the different subsystems 112-118 described herein is forillustrative purposes, and is not intended to be limiting, as any ofsubsystems 112-118 may provide more or less functionality than isdescribed. For example, one or more of subsystems 112-118 may beeliminated, and some or all of its functionality may be provided byother ones of subsystems 112-118. As another example, additionalsubsystems may be programmed to perform some or all of the functionalityattributed herein to one of subsystems 112-118.

Although the present invention has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred embodiments, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed embodiments, but, on the contrary, is intendedto cover modifications and equivalent arrangements that are within thescope of the appended claims. For example, it is to be understood thatthe present invention contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A method comprising: detecting, based on chat activity information ofa user in a chat interface, an indication of a failure in providing aresolution for the user; obtaining a document associated with the userbased on a predicted intent of the user, wherein the predicted intent isdetermined based on one or more user inputs in the chat activityinformation; and in response to the detected failure indication,providing one or more document portions of the document for presentationon the chat interface in connection with providing the resolution.

2. The method of embodiment 1, wherein detecting the failure includes:obtaining a first user input of a user via the chat interface during achat session with the user; determining the predicted intent based onthe first user input.

3. The method of embodiment 2, further comprising: generating, via thechat interface, a first response related to the first user input basedon the predicted intent.

4. The method of any of embodiments 1-3, wherein detecting the failureincludes: obtaining one or more second user inputs of the usersubsequent the first response; and detecting, based on the one or moresecond user inputs, an indication of a failure in providing a resolutionfor the user.

5. The method of any of embodiments 1-4, wherein detecting the failureincludes: detecting that a number of user input words or sentencesrelated to seeking the resolution satisfies an amount threshold.

6. The method of any of embodiments 1-5, wherein detecting the failureincludes: detecting that a duration of the chat session satisfies aduration threshold.

7. The method of any of embodiments 1-6, wherein detecting the failureincludes: detecting a negative sentiment in connection with the one ormore user inputs in the chat activity information.

8. The method of any of embodiments 1-7, further comprising: in responseto detecting the indication of the failure, presenting a graphical userinterface element in the chat interface for facilitating the user toupload a specified document.

9. The method of any of embodiments 1-8, wherein providing the one ormore document portions includes: selecting the one or more documentportions over one or more other document portions based on the one ormore document portions being a greater match with the predicted intentthan the one or more other document portions.

10. The method of any of embodiments 1-9, wherein providing the one ormore document portions includes: presenting, based on the selection, animage depicting the one or more document portions.

11. The method of any of embodiments 9-10, wherein providing the one ormore document portions includes: presenting the document via the chatinterface, wherein the selected one or more document portions isemphasized relative to other one or more document portions.

12. The method of any of embodiments 1-11, wherein obtaining thedocument includes: obtaining metadata of each of a plurality ofdocuments associated with the user based on content extracted from eachof the documents, and obtaining the document from the documents based onmetadata of the document matching the predicted intent.

13. The method of any of embodiments 1-12, further comprising: providingthe document to a prediction model to obtain concepts associated withdocument portions of the document; determining a probability of adocument portion matching the predicted intent based on the conceptsassociated with the document portion; and selecting one or more documentportions of the document over one or more other document portions of thedocument based on probabilities associated with the one or more documentportions being greater than the probabilities associated with the one ormore other document portions.

14. The method of any of embodiments 1-13, wherein obtaining thedocument includes: providing a plurality of documents associated with auser to a prediction model to obtain concepts associated with each ofthe documents; determining a probability of each of the documentsmatching the predicted intent based on the concepts associated with thedocument; and selecting one or more documents over one or more otherdocuments based on probabilities associated with the one or moredocuments being greater than the probabilities associated with the oneor more other documents.

15. The method of any of embodiments 1-14, wherein obtaining thedocument includes: obtaining a document set using a prediction model,wherein each document of the document set is associated with aprobability of matching a current intent of the user; and selecting oneor more documents from the document set based on the one or moredocuments being associated with higher probabilities than one or moreother documents of the intent set.

16. The method of any of embodiments 1-15, wherein obtaining thedocument includes: providing one or more of a plurality of documentsassociated with the user to a prediction model to generate a documentbased on the one or more of the plurality of documents.

17. The method of any of embodiments 1-16, further comprising: obtainingan intent set using a prediction model, wherein each intent of theintent set is associated with a probability of matching a current intentof the user; and selecting one or more predicted intents from the intentset based on the one or more predicted intents being associated withhigher probabilities than one or more other predicted intents of theintent set.

18. The method of any of embodiments 13-17, wherein the prediction modelcomprises a neural network or other machine learning model.

19. A tangible, non-transitory, machine-readable medium storinginstructions that, when executed by a data processing apparatus, causesthe data processing apparatus to perform operations comprising those ofany of embodiments 1-18.

20. A system comprising: one or more processors; and memory storinginstructions that, when executed by the processors, cause the processorsto effectuate operations comprising those of any of embodiments 1-18.

1. A system for facilitating a virtual-assistant-based resolution ofuser inquiries via negative-feedback-triggered document presentation,the system comprising: a computer system that comprises one or moreprocessors programmed with computer program instructions that, whenexecuted, cause operations comprising: obtaining a first user input of auser via a chat interface; determining a predicted intent of the userbased on the first user input; generating, via the chat interface, afirst response related to the first user input based on the predictedintent; in response to detecting negative feedback related to the firstresponse in connection with one or more second user inputs following thefirst response, selecting, for presentation via the chat interface, oneor more document portions of a document over one or more other documentportions based on the one or more document portions being a greatermatch with the predicted intent than the one or more other documentportions; and presenting the one or more document portions of thedocument via the chat interface.
 2. The system of claim 1, wherein theoperations further comprise: providing the document to a predictionmodel to obtain concepts associated with document portions of thedocument, the concepts comprising one or more first concepts associatedwith the one or more document portions and one or more second conceptsassociated with the one or more other document portions of the document;determining one or more first probabilities of the one or more documentportions matching the predicted intent based on the one or more firstconcepts and one or more second probabilities of the one or more otherdocument portions matching the predicted intent based on the one or moresecond concepts; and selecting the one or more document portions overthe one or more other document portions based on the one or more firstprobabilities being greater than the one or more second probabilities.3. A method comprising: obtaining a first user input of a user via achat interface; determining a predicted intent of the user based on thefirst user input; generating, via the chat interface, a first responserelated to the first user input based on the predicted intent; and inresponse to detecting negative feedback related to the first response inconnection with one or more second user inputs following the firstresponse, providing one or more document portions of a document via thechat interface, wherein the one or more document portions of thedocument are selected for presentation via the chat interface over oneor more other document portions based on the one or more documentportions being a greater match with the predicted intent than the one ormore other document portions.
 4. The method of claim 3, furthercomprising: providing the document to a prediction model to obtainconcepts associated with document portions of the document, the conceptscomprising one or more first concepts associated with the one or moredocument portions and one or more second concepts associated with theone or more other document portions of the document; determining one ormore first probabilities of the one or more document portions matchingthe predicted intent based on the one or more first concepts and one ormore second probabilities of the one or more other document portionsmatching the predicted intent based on the one or more second concepts;and selecting the one or more document portions over the one or moreother document portions based on the one or more first probabilitiesbeing greater than the one or more second probabilities.
 5. The methodof claim 3, wherein providing the one or more document portionscomprises presenting, based on the selection of the one or more documentportions, an image depicting the one or more document portions.
 6. Themethod of claim 3, wherein providing the one or more document portionscomprises: presenting the document via the chat interface; andemphasizing, based on the selection of the one or more documentportions, the one or more document portions over the one or more otherdocument portions of the document during the presentation of thedocument.
 7. The method of claim 3, further comprising: performingsentiment analysis monitoring on the one or more second user inputs todetect a negative sentiment in connection with the one or more seconduser inputs, wherein providing the one or more document portionscomprises providing the one or more document portions of the documentvia the chat interface in response to detecting the negative sentimentin connection with the one or more second user inputs.
 8. The method ofclaim 3, further comprising: in response to detecting the negativefeedback related to the first response, presenting a graphical userinterface element in the chat interface for facilitating the user toupload a specified document.
 9. The method of claim 3, furthercomprising: obtaining metadata of each of a plurality of documentsassociated with the user based on content extracted from each of theplurality of documents, and obtaining the document from the plurality ofdocuments based on metadata of the document matching the predictedintent.
 10. The method of claim 3, wherein determining the predictedintent comprises: obtaining an intent set using a prediction model,wherein each predicted intent of the intent set is associated with aprobability of matching a current intent of the user, and selecting thepredicted intent from the intent set based on the predicted intent beingassociated with a higher probability than other predicted intents of theintent set.
 11. The method of claim 3, further comprising: generatingthe document based on a plurality of documents associated with the user.12. One or more non-transitory computer-readable media comprisinginstructions that, when executed by one or more processors, causeoperations comprising: obtaining a first user input of a user via a userinterface; determining a predicted intent of the user based on the firstuser input; generating, via the user interface, a first response relatedto the first user input based on the predicted intent; and in responseto detecting negative feedback related to the first response inconnection with one or more second user inputs following the firstresponse, providing one or more document portions of a document via theuser interface, wherein the one or more document portions of thedocument are selected for presentation via the user interface over oneor more other document portions based on the one or more documentportions being a greater match with the predicted intent than the one ormore other document portions.
 13. The media of claim 12, the operationsfurther comprising: providing the document to a prediction model toobtain concepts associated with document portions of the document, theconcepts comprising one or more first concepts associated with the oneor more document portions and one or more second concepts associatedwith the one or more other document portions of the document;determining one or more first probabilities of the one or more documentportions matching the predicted intent based on the one or more firstconcepts and one or more second probabilities of the one or more otherdocument portions matching the predicted intent based on the one or moresecond concepts; and selecting the one or more document portions overthe one or more other document portions based on the one or more firstprobabilities being greater than the one or more second probabilities.14. The media of claim 12, wherein providing the one or more documentportions comprises presenting, based on the selection of the one or moredocument portions, an image depicting the one or more document portions.15. The media of claim 12, wherein providing the one or more documentportions comprises: presenting the document via the user interface; andemphasizing, based on the selection of the one or more documentportions, the one or more document portions over the one or more otherdocument portions of the document during the presentation of thedocument.
 16. The media of claim 12, the operations further comprising:performing sentiment analysis monitoring on the one or more second userinputs to detect a negative sentiment in connection with the one or moresecond user inputs, wherein providing the one or more document portionscomprises providing the one or more document portions of the documentvia the user interface in response to detecting the negative sentimentin connection with the one or more second user inputs.
 17. The media ofclaim 12, the operations further comprising: in response to detectingthe negative feedback related to the first response, presenting agraphical user interface element in the user interface for facilitatingthe user to upload a specified document.
 18. The media of claim 12, theoperations further comprising: obtaining metadata of each of a pluralityof documents associated with the user based on content extracted fromeach of the plurality of documents, and obtaining the document from theplurality of documents based on metadata of the document matching thepredicted intent.
 19. The media of claim 12, wherein determining thepredicted intent comprises: obtaining an intent set using a predictionmodel, wherein each predicted intent of the intent set is associatedwith a probability of matching a current intent of the user, andselecting the predicted intent from the intent set based on thepredicted intent being associated with a higher probability than otherpredicted intents of the intent set.
 20. The media of claim 12, theoperations further comprising: generating the document based on aplurality of documents associated with the user.